博碩士論文 964203033 詳細資訊


姓名 王建堯(Chien-Yao Wang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 利用使用者興趣檔探討形容詞所處位置對評論分類的重要性
(Applying User Profile to Explore the Importance of Adjectives’ Position in Review Classification)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 ( 永不開放)
摘要(中) 隨著強調使用者參與的Web 2.0之蓬勃發展,越來越多Blog、論壇興起,也越來越多使用者會在網路上發表自己的評論,像是電影評論或是產品評論。這些線上評論對於人們而言,也變得是個重要且有用的資訊來源。然而,網路上的評論可能會多如牛毛,為了讓使用者能更輕鬆地閱讀評論,這些評論必須要經過處理,例如把這些評論自動分成正面評論、負面評論兩類,或是進行意見探勘。
在評論分類及意見探勘的研究上,形容詞是相當重要的因子。評論內的形容詞通常可以顯示出該評論的正、負面傾向。因此,以往的研究常以形容詞的正、負向及其出現頻率作為評論分類的基礎。為了提升對評論資訊應用的效率,本研究希望能擴展形容詞的可用特性。由於資訊擷取領域的相關研究顯示,文字出現在文章中不同的段落位置對文章之重要性有不同的意義。我們因此思考,評論中的形容詞在文章中所出現的位置,是否對評論而言也有不同的重要性?
為了了解相關問題,本研究利用建立使用者興趣檔的概念,建構出正、負面評論的Opinion清單,其中輔以字詞權重的計算來區別各Opinion的重要性,期能以此來探索形容詞所處評論中的位置是否會對評論分類有所影響,而實驗結果也證實,形容詞在文章中的位置,確實在評論分類上是個具有功效的因子。
摘要(英) With the rapid developing of Web 2.0, more and more blogs and forums appear, and more and more people post reviews on the Web. The online review is becoming a useful and important information resource for people.
However, the number of reviews can be in hundreds. These reviews must be processed so that user may read them easily, e.g. classifying them automatically into two polarities (positive and negative), or doing opinion mining.
Adjective is an important factor in review classification and opinion mining. The adjectives in a review can usually show the orientation of the review, either positive or negative. Therefore, the semantic orientation of adjectives and the term frequency of adjectives are the basis of review classification in such researches.
In order to increase the performance of the review classification, this research has aimed to expand the useful attribute of adjectives. Some researches of Information Retrieval show that the words in different paragraphs have different meaning to the document, so this research considers whether the adjectives in different paragraphs have different meaning to the review. To examine this adjective characteristic, this research has applied user profiles to under manage the position of the adjective to uncover its influence on the accuracy of review classification. The experimental results show that the position of adjectives in reviews is an effective factor on review classification.
關鍵字(中) ★ 使用者興趣檔
★ 意見探勘
★ 評論分類
關鍵字(英) ★ review classification
★ opinion mining
★ user profile
論文目次 第1章 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究範圍與限制 2
1-4 研究流程 3
1-5 論文架構 4
第2章 文獻探討 5
2-1 意見探勘 5
2-2 評論分類 8
2-3 字詞權重 11
2-4 使用者興趣檔 14
第3章 系統設計 16
3-1 系統流程 16
3-2 系統架構 24
第4章 實驗分析 28
4-1 實驗設計與流程 28
4-2 實驗結果與分析 30
第5章 結論 37
5-1 研究結論與貢獻 37
5-2 未來研究方向 38
參考文獻 40
參考文獻 英文部分
[1] Annett, M. and Kondrak, G. (2008), “A comparison of sentiment analysis techniques: Polarizing movie blogs,” Lecture Notes in Computer Science, Vol.5032, pp. 25-35.
[2] Belkin, N. J. and Bruce Croft, W. (1992), “Information filtering and information retrieval: Two sides of the same coin?” Communications of the ACM, Vol.35 No.12, pp. 29-38.
[3] Chaovalit, P. and Zhou, L. (2005), “Movie review mining: A comparison between supervised and unsupervised classification approaches,” Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii, January 2005, pp. 112.3.
[4] Church, K., Gale, W., Hanks, P. and Hindle, D. (1989), “Parsing, word associations and typical predicate-argument relations,” Proceedings of the Workshop on Speech and Natural Language, Cape Cod, Massachusetts, October 1989, pp. 75-81.
[5] De Marneffe, M. C. and Manning, C. D. (2008), “Stanford typed dependencies manual,” available at:
http://www-nlp.stanford.edu/software/dependencies_manual.pdf
[6] Ding, X., Liu, B. and Yu, P. S. (2008), “A holistic lexicon-based approach to opinion mining,” Proceedings of the International Conference on Web Search and Web Data Mining, Palo Alto, California, U.S.A., February 2008, pp. 231-240.
[7] Fellbaum, C. (Ed.) (1998), WordNet: An Electronic Lexical Database, MIT Press, Cambridge, MA.
[8] Fresno, V. and Ribeiro A. (2004), “An analytical approach to concept extraction in HTML environments,” Journal of Intelligent Information Systems, Vol.22 No.3, pp. 215-235.
[9] Hatzivassiloglou, V. and Mckeown, K. (1997), “Predicting the semantic orientation of adjectives,” Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the European Chapter of the ACL, Madrid, Spain, July 1997, pp. 174-181.
[10] Hatzivassiloglou, V. and Wiebe, J. M. (2000), “Effects of adjective orientation and gradability on sentence subjectivity,” Proceedings of the 18th Conference on Computational Linguistics, Saarbrucken, Germany, July 2006, pp. 299-305.
[11] Hu, M. and Liu, B. (2004), “Mining opinion features in customer reviews,” Proceedings of Nineteenth National Conference on Artificial Intelligence, San Jose, U.S.A., July 2004.
[12] Hu, M. and Liu, B. (2004), “Mining and summarizing customer reviews,” Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle W.A., U.S.A., August 2004, pp. 168-177.
[13] Kamps, J. and Marx, M. (2002), “Words with attitude,” Proceedings of the First International Conference on Global WordNet, Mysore, India, January 2002, pp. 332-341.
[14] Kim, S. M. and Hovy, E. (2006), “Automatic identification of pro and con reasons in online reviews,” Proceedings of the COLING/ACL on Main Conference Poster Sessions, Sydney, Australia, July 2006, pp. 483-490.
[15] Lavelli, A., Sebastiani F. and Zanoli, R. (2004), “Distributional term representations: An experimental comparison,” Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, Washington D.C., U.S.A., November 2004, pp. 615-624.
[16] Liu, B., Hu, M. and Cheng, J. (2005), “Opinion observer: Analyzing and comparing opinions on the web,” Proceedings of the 14th International Conference on World Wide Web, Chiba, Japan, May 2005, pp. 342-351.
[17] Miller, G., Beckwith, R., Fellbaum, C., Gross, D. and Miller, K. (1990), “Introduction to WordNet: An on-line lexical database,” International Journal of Lexicography, Vol.3 No.4, pp. 235-312.
[18] Pang, B., Lee, L. and Vaithyanathan, S. (2002), “Thumbs up? Sentiment classification using machine learning techniques,” Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Philadelphia P.A., U.S.A., July 2003, pp. 279-286.
[19] Popescu, A. M. and Etzioni, O. (2005), “Extracting product features and opinions from reviews,” Proceedings of HLT '05 Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver B.C., Canada, October 2005, pp. 339-346.
[20] Rich, E. A. (1979), “User modeling via stereotypes,” Cognitive Science, Vol.3 No.4, pp. 329-354.
[21] Salton, G. and Buckley, C. (1988), “Term-weighting approaches in automatic text retrieval,” Information Processing & Management, Vol.24 No.5, pp. 513-523.
[22] Salton, G. and Leck, M. E. (1968), “Computer evaluation of indexing and text processing,” Journal of the ACM, Vol.15 No.1, pp. 8-36.
[23] Turney, P. D. (2001), “Mining the web for synonyms: PMI-IR versus LSA on TOEFL,” Proceedings of the Twelfth European Conference on Machine Learning, Freiburg, Germany, September 2001, pp. 491-502.
[24] Turney, P. D. (2002), “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews,” Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, July 2002, pp. 417-424.
[25] Turney, P. D. and Littman, M. L. (2003), “Measuring praise and criticism: Inference of semantic orientation from association,” ACM Transactions on Information Systems, Vol.21 No.40, pp. 315-346.
[26] Wiebe, J. M. (2000), “Learning subjective adjectives from corpora,” Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, Austin, Texas, U.S.A., July 2000, pp. 735-741.
[27] Wiebe J. M. and Riloff E. (2005), “Creating subjective and objective sentence classifiers from un-annotated tests,” Proceedings of the Sixth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, Mexico, February 2005, pp. 486-497.
[28] Ye, Q., Lin, B. and Li, Y. J. (2005), “Sentiment classification for Chinese reviews: A comparison between SVM and semantic approaches,” Proceedings of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, China, August 2005, pp. 2341-2346.
[29] Yu, H. and Hatzivassiloglou, V. (2003), “Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences,” Proceedings of the 8th Conference on Empirical Methods in Natural Language Processing, Sapporo, Japan, July 2003, pp. 129-136.
[30] Zhuang, L., Jing, F. and Zhu, X. Y. (2006), “Movie review mining and summarization,” Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Arlington, Virginia, U.S.A., November 2006, pp. 43-50.
中文部分
[31] 張永霖,2002,使用基因演算法與相關回饋於協助網頁搜尋,國立中央大學資訊管理研究所碩士論文。
[32] 張振超,2006,結合基因演算法與使用者興趣檔之資訊檢索研究,國立中央大學資訊管理研究所碩士論文。
指導教授 周世傑(Shih-Chieh Chou) 審核日期 2009-7-2
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡